Effect of Coniferous Tree–Shrub Mixtures on Traffic Noise Reduction in Public Spaces

Despite the well-established ability of urban green belts to reduce traffic noise, a comprehensive analysis of the specific role played by mixed coniferous trees and shrubs in noise mitigation remains lacking. This study aimed to clarify how different planting patterns and the characteristics of plants affect their noise-reduction performance. To achieve this, noise reduction was measured at 18 roadside green spaces comprising mixed coniferous trees and shrubs in Harbin, China, and Moscow, Russia. The results indicate that in lanes 5–15 m wide, the ‘Abreast’ planting pattern consistently offered greater noise reduction than the ‘Taffy’ configuration at all measured distances (5, 10 and 15 m). In addition, in winter the effectiveness of noise reduction improved due to snow cover, which enhanced the sound-absorbing properties of the vegetation. In our analysis, key factors such as diameter at breast height, minimum height under branches and road width emerged as crucial predictors of traffic noise reduction. Among these, carriageway width and sidewalk width exhibited the strongest correlations with noise attenuation. Finally, we developed a quantitative model for roadside green spaces that incorporates plant characteristics, planting schemes and road features. This model allows us to assess the contribution of each factor to overall noise reduction. The results of this study provide a scientific basis for designing and optimising vegetation-based noise-mitigation strategies to enhance the urban acoustic environment while also offering an analytical framework to support evidence-based urban forestry planning and policy.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2025-11-26
DOI
https://doi.org/10.3390/buildings15234266
Primary Topic
Noise Effects and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effect of Coniferous Tree–Shrub Mixtures on Traffic Noise Reduction in Public Spaces

Qi Meng, Mengmeng Li, Olga Evgrafova
Buildings
Noise Effects and Management
article

Effect of Coniferous Tree–Shrub Mixtures on Traffic Noise Reduction in Public Spaces

Qi Meng, Mengmeng Li, Olga Evgrafova
article en

Abstract

Despite the well-established ability of urban green belts to reduce traffic noise, a comprehensive analysis of the specific role played by mixed coniferous trees and shrubs in noise mitigation remains lacking. This study aimed to clarify how different planting patterns and the characteristics of plants affect their noise-reduction performance. To achieve this, noise reduction was measured at 18 roadside green spaces comprising mixed coniferous trees and shrubs in Harbin, China, and Moscow, Russia. The results indicate that in lanes 5–15 m wide, the ‘Abreast’ planting pattern consistently offered greater noise reduction than the ‘Taffy’ configuration at all measured distances (5, 10 and 15 m). In addition, in winter the effectiveness of noise reduction improved due to snow cover, which enhanced the sound-absorbing properties of the vegetation. In our analysis, key factors such as diameter at breast height, minimum height under branches and road width emerged as crucial predictors of traffic noise reduction. Among these, carriageway width and sidewalk width exhibited the strongest correlations with noise attenuation. Finally, we developed a quantitative model for roadside green spaces that incorporates plant characteristics, planting schemes and road features. This model allows us to assess the contribution of each factor to overall noise reduction. The results of this study provide a scientific basis for designing and optimising vegetation-based noise-mitigation strategies to enhance the urban acoustic environment while also offering an analytical framework to support evidence-based urban forestry planning and policy.

BuildingsVol. 15(23)
Harbin Institute of Technology (CN), Ministry of Industry and Information Technology (CN)
Openalex Percentile: Top 53%
Noise Effects and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.